CONTENT BASED LEAF IMAGE RETRIEVAL (CBLIR) USING SHAPE, COLOR AND TEXTURE FEATURES

This paper proposes an efficient computer-aided Plant Image Retrieval method based on plant leaf images using Shape, Color and Texture features intended mainly for medical industry, botanical gardening and cosmetic industry. Here, we use HSV color space to extract the various features of leaves. Log-Gabor wavelet is applied to the input image for texture feature extraction. The Scale Invariant Feature Transform (SIFT) is incorporated to extract the feature points of the leaf image. Scale Invariant Feature Transform transforms an image into a large collection of feature vectors, each of which is invariant to image translation, scaling, and rotation, partially invariant to illumination changes and robust to local geometric distortion. SIFT has four modules namely detection of scale space extrema, local extrema detection, orientation assignment and key point descriptor. Results on a database of 500 plant images belonging to 45 different types of plants with different orientations scales, and translations show that proposed method outperforms the recently developed methods by giving 97.9% of retrieval efficiency for 20, 50, 80 and 100 retrievals.

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